One important component of hematology is the differential blood count. Systems from the field of “computer-assisted microscopy” (CAM) enable automatic analysis of blood smears and support hematologists in classifying cells, and thus they form a supplement for modern hematological laboratory diagnostics. It is in a fast, precise and highly efficient manner that modern hematology systems provide important information about the cell population of peripheral blood. However, up to 40% of samples may subsequently be manually differentiated under the microscope in clinics and laboratories. Specifically this last step may be accelerated and objectified by means of a system of “computer-assisted microscopy”, as is shown in FIG. 11. This leads to both reducing the amount of work involved and increasing the quality of the results. On the basis of innovative concepts of image processing, leukocytes in the blood smear are localized and classified into clinically relevant subclasses. Reference data sets which are classified in advance by experts and may be extended any time serve as the basis for said classification.
A typically CAM system (as is shown by way of example of a system for creating a differential blood count in FIG. 11) typically consists of the following core modules: detection of the cells, segmentation of the cells, and classification of the cells.
In particular in bone marrow smears, cells (typically white blood corpuscles—leukocytes) are mostly exist in the form of cell clusters (cell groups), i.e. the individual cells are directly adjacent to one another and are therefore difficult to segment, which complicates exact differentiation. Several methods addressing segmentation of leukocytes in bone marrow smears have been known from the literature. The majority of methods are based on the watershed algorithm. The documents LEHMANN; T., W. OBERSCHELP; E. PELIKAN and R. REPGES: Bildverarbeitung für die Medizin, Springer-Verlag, 1997, GONZALES, R. C., and R. E. WOODS: Digital Image Processing (3rd Edition), Prentice-Hall, Inc., Upper Saddle River, N.J., USA, 2006, show an application of this watershed algorithm in digital image processing. The most widely used methods of segmenting leukocytes in bone marrow smears will be mentioned below.
The documents NILSSON, B, and A. HEYDEN: Model-based Segmentation of Leukocytes Clusters. In: ICPR '02: Proceedings of the 16th International Conference on Pattern Recognition (ICPR, 02, Volume 1, page 10,727, Washington, D.C., USA, 2002. IEEE Computer Society, and NILSSON, B., and A. HEYDEN: Segmentation of complex cell clusters in microscopic images: application to bone marrow samples. Cytometry, 66(1): 24-31, 2005, show a method of segmenting complex cell clusters in microscopic images. By means of this method, the cell clusters are initially separated from the background by a threshold-value method. In order to separate leukocytes within a cluster, the background segmentation is subjected to weighted distance transformation, and the result is subdivided into regions by means of the watershed algorithm. Since the watershed step results in over-segmentation, adjacent regions are merged on the basis of such features as “roundness”, “surface area” etc. The result is the segmentation of cells.
The document PARK, J., and J. KELLER: Snakes on the Watershed. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10): 1,201-1,205, 2001, shows a so-called snakes-on-the-watershed method. As in the preceding method, distance transformation is applied to background segmentation. The output of the subsequent watershed algorithm is used for initializing the “snake” zones of the “snake” algorithm. By means of the “snake” algorithm, the contour of each leukocyte is then determined.
In the method of the document PARK, J.-S., and J. KELLER: Fuzzy patch label relaxation in bone marrow cell segmentation, in: Systems, Man, and Cybernetics. 1997. ‘Computational Cybernetics and Simulation’, 1997 IEEE International Conference on, Volume 2, pages 1,133-1,138, Volume 2, October 1997, the watershed algorithm is applied directly to the input image. By means of stochastic methods, the resulting regions are associated with the four classes of background, red blood corpuscles (erythrocytes), cytoplasm and cell nucleus.
In the method of the document JIANHUA, W., Z. LI, L. YANGBIN and Z. PINGPING: Image Segmentation Method based on Lifting Wavelet and Watershed Arithmetic, in: Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on, pages 2-978-2-981, 16., 18. July 2007, 2007, images having lower resolutions are generated from the input image by means of so-called wavelets (which developed when wavelet transformation was employed). Said images are then segmented with the aid of the watershed algorithm. The segmentations calculated at the different stages of resolution are combined to obtain a high-quality segmentation result of the original image.
In the following, mention shall also be made of two further methods not based on the above-mentioned watershed algorithm. The document HENGEN, H., S. L. SPOOR and M. C. PANDIT: Analysis of blood and bone marrow smears using digital image processing techniques, in: M. SONKA & J. M. FITZPATRICK (Eds.): Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Volume 4.684 of the series Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference, pages 624-635, May 2002, shows a method wherein segmentation is performed in that a distance transformation is calculated on the basis of a background estimation. By means of a threshold value, regions are then generated on the distance image, said regions representing the midpoints of the cells, for example. Said regions are then used for initializing a “region growing” algorithm so as to find the boundaries of the cells.
The document MONTSENY, E., P. SOBREVILLA and S. ROMANI: A fuzzy approach to white blood cells segmentation in color bone marrow images, in: Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on, Volume 1, pages 173-178, Volume 1, July 2004, shows a method wherein each pixel is associated with one of 53 color patterns. With the aid of stochastic methods, each color pattern has one of three classes assigned to it, and thus classification of each pixel is achieved. The three classes are called region of interest, undefined region, and region not of interest.
All of the methods introduced herein have the disadvantage that reliable segmentation of cells that are present in cell clusters is not effected or is effected only insufficiently. In particular methods based on the watershed algorithm tend to subdivide one cell into several individual regions, i.e. a cell is over-segmented.